Awesome

SymTrans: Symmetric Transformer-based model for image registration

Paper:

Symmetric Transformer-based Network for Unsupervised Image Registration

Please cite: https://arxiv.org/abs/2204.13575

The proposed SymTrans architecture：

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The GPU memory occupied during training using our SymTrans is about 3 GB with a batch size of 1 on our server. Vit-V-Net and Transmorph occupy about 6 GB and 7 GB of GPU memory. All the images are size of [96,112,96]. The parameters and FLOPs are following：

Trained model

We uploaded the weights, including the displacement and diffeomorphic registration model's weights.

Training

If you would like to train this model on your own dataset, conver you data to numpy.array (i.e. .npy) format, then put them in /Data/train_data/. To validate the training process, put the validation data in /Data/validation_data/. In detail, put the atlases to /Data/validation_data/atlases/; put the atlases' labels to /Data/validation_data/atlases_label/. Correspondingly, put moving (source) images and their labels in /Data/validation_data/valsets/ and /Data/validation_data/valsets_label/.

Excute this comand train the SymTran after allocate the dataset：

python train.py

Due to the GPU memory limitation, we could not train SymTrans and baseline approaches on the larger shaped images. If you would like to train the images shaped of [160,192,224], please enlarge the sr_ratio in the train.py to get better results.

Test

Checkpoints and training logs, including validation results and loss values, are recorded in the./Chekcpoint/ and './Log/' folder. You can use tensorboardx to moniter the training. Using the parameter --learning_mode to select diffeomorphic or displacement registration (default --learning_mode displacement).

All the parameters can be found in the train.py and test.py. You can modify them if you would like to configure your own training or testing.